In this paper, we proposed two new architectural enhancements with respect to the network proposed in [1]}: a new bounding box IoU classification task to filter out errors on pseudo-labels produced by the Teacher and the introduction of the unsupervised regression losses.
For the former, we introduced a lightweight branch to predict the bounding box IoU quality.
For the latter, we demonstrated how to successfully integrate it in the training, balancing the training tasks.
Our new model called IL-net, which contains both, increases the general SSOD performance by a 1.14% AP on COCO dataset in a limited annotation regime.
